Pandas5 Completed

[alokusw2]

No description provided.

[super30admin]

1. Problem 1: Department Highest Salary

   • Correctness: The solution correctly identifies employees with the highest salary in each department by merging the tables, grouping by department, and finding max salaries. This approach is correct.
   • Time Complexity: O(n + m) for the merge, O(n) for the groupby and transform, where n is the number of employees and m is the number of departments. This is efficient.
   • Space Complexity: O(n + m) due to the merged DataFrame.
   • Code Quality: The code is clean and readable. The use of transform is appropriate here. Column renaming is clear.
   • Efficiency: The solution is already efficient, but could consider filtering before merging if departments are large.

2. Problem 2: Rank Scores

   • Correctness: The solution correctly ranks scores using dense ranking, which handles ties appropriately. The sorting at the end ensures the output is in descending order.
   • Time Complexity: O(n log n) due to the ranking and sorting operations.
   • Space Complexity: O(n) for storing the rank column.
   • Code Quality: The code is concise and clear. The use of pandas' rank method with 'dense' is appropriate.
   • Efficiency: The solution is efficient for typical use cases. No major optimizations needed.

General Observations:

• Both solutions follow pandas best practices and are well-structured.
• The student demonstrates good understanding of pandas operations like merge, groupby, transform, and rank.
• Variable names could be more descriptive (e.g., 'name_y' and 'name_x' could be renamed to 'department_name' and 'employee_name' for clarity).
• Documentation or comments explaining the steps could improve maintainability.

[super30admin]

1. Correctness:

   • Problem 1: The solution correctly identifies employees with the highest salary in each department by merging the employee and department tables, grouping by department, and finding the max salary. The approach is correct.
   • Problem 2: The solution correctly ranks scores using the 'dense' method, which handles ties appropriately and avoids gaps in ranking. The result is sorted by score in descending order as required.

2. Time Complexity:

   • Problem 1: The merge operation is O(N + M), where N and M are the sizes of the employee and department tables. The groupby and transform operations are O(N log N) due to sorting. Overall, it's efficient for typical dataset sizes.
   • Problem 2: The rank operation is O(N log N) due to sorting, and the final sort is also O(N log N). This is optimal for this problem.

3. Space Complexity:

   • Problem 1: The merge creates a new DataFrame, so space complexity is O(N + M). This is reasonable given the problem requirements.
   • Problem 2: The operation is done in-place with minimal additional space, so space complexity is O(1) for operations, but O(N) for storing the result.

4. Code Quality:

   • The code is well-structured and readable. Variable names are descriptive (e.g., 'merged', 'max_salaries').
   • For Problem 1, the column renaming at the end is clear and follows best practices.
   • For Problem 2, the use of pandas' built-in rank function with 'dense' method is idiomatic and efficient.

5. Efficiency:

   • Both solutions are efficient and use pandas operations optimally. No significant optimizations are needed.
   • One minor improvement for Problem 1 could be to use 'departmentId' directly in the groupby instead of 'name_y' to avoid potential ambiguity if department names are not unique.